create the block of eca in Dev 1.x

[wqe123321]

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Motivation

I want to add an attention module to the Neck part, in order to enhance the attention to features.

Modification

code is following：

import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16

from ..builder import NECKS

from mmrotate.models.builder import ROTATED_NECKS

class eca_layer(nn.Module):#eca注意力
def init(self, channel, k_size=3):
super(eca_layer, self).init() # super类的作用是继承的时候，调用含super的哥哥的基类__init__函数。
self.avg_pool = nn.AdaptiveAvgPool2d(1) # 全局平均池化
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size,
padding=(k_size - 1) // 2, bias=False) # 基于1*1卷积学习通道之间的信息
self.sigmoid = nn.Sigmoid() # 激活函数

def forward(self, x):
    # x: input features with shape [b, c, h, w]
    b, c, h, w = x.size()  # b代表b个样本，c为通道数，h为高度，w为宽度
    # feature descriptor on the global spatial information
    y = self.avg_pool(x)
    # Two different branches of ECA module
    # torch.squeeze()这个函数主要对数据的维度进行压缩,torch.unsqueeze()这个函数 主要是对数据维度进行扩充
    y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
    # Multi-scale information fusion多尺度信息融合
    y = self.sigmoid(y)
    # 原网络中克罗内克积，也叫张量积，为两个任意大小矩阵间的运算
    return x * y.expand_as(x)

@ROTATED_NECKS.register_module()
class ECA(BaseModule): #eca注意力
def init(self,
in_channels,
out_channels,
num_outs,
start_level=0,
end_level=-1,
add_extra_convs=False,
relu_before_extra_convs=False,
no_norm_on_lateral=False,
conv_cfg=None,
norm_cfg=None,
act_cfg=None,
upsample_cfg=dict(mode='nearest'),
init_cfg=dict(
type='Xavier', layer='Conv2d', distribution='uniform')):
super(FPN_eca, self).init(init_cfg)
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_ins = len(in_channels)
self.num_outs = num_outs
self.relu_before_extra_convs = relu_before_extra_convs
self.no_norm_on_lateral = no_norm_on_lateral
self.fp16_enabled = False
self.upsample_cfg = upsample_cfg.copy()

    if end_level == -1 or end_level == self.num_ins - 1:
        self.backbone_end_level = self.num_ins
        assert num_outs >= self.num_ins - start_level
    else:
        # if end_level is not the last level, no extra level is allowed
        self.backbone_end_level = end_level + 1
        assert end_level < self.num_ins
        assert num_outs == end_level - start_level + 1
    self.start_level = start_level
    self.end_level = end_level
    self.add_extra_convs = add_extra_convs
    assert isinstance(add_extra_convs, (str, bool))
    if isinstance(add_extra_convs, str):
        # Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output'
        assert add_extra_convs in ('on_input', 'on_lateral', 'on_output')
    elif add_extra_convs:  # True
        self.add_extra_convs = 'on_input'

    self.lateral_convs = nn.ModuleList()
    self.fpn_convs = nn.ModuleList()
    self.fpn_att = nn.ModuleList()#ModeleList,可以存放很多函数，调用的时候类似数组用下标调用，比如fpn_att[i]


    for i in range(self.start_level, self.backbone_end_level):#遍历特征图，为了获取特征图通道数
        l_conv = ConvModule(
            in_channels[i],  # 输入通道数
            out_channels,#输出通道数
            1,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
            act_cfg=act_cfg,
            inplace=False)
        fpn_conv = ConvModule(
            out_channels,
            out_channels,
            3,
            padding=1,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg,
            inplace=False)
        s_layer = eca_layer(out_channels)

        self.lateral_convs.append(l_conv)
        self.fpn_convs.append(fpn_conv)
        self.fpn_att.append(s_layer)#容器fpn_att存放了每一层特征图的注意力函数，因为每层注意力输入通道数不一样

#包含了每一个特征图的操作
# add extra conv layers (e.g., RetinaNet)
extra_levels = num_outs - self.backbone_end_level + self.start_level
if self.add_extra_convs and extra_levels >= 1:
for i in range(extra_levels):
if i == 0 and self.add_extra_convs == 'on_input':
in_channels = self.in_channels[self.backbone_end_level - 1]
else:
in_channels = out_channels
extra_fpn_conv = ConvModule(
in_channels,
out_channels,
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
inplace=False)
self.fpn_convs.append(extra_fpn_conv)

@auto_fp16()
def forward(self, inputs):
    """Forward function."""
    assert len(inputs) == len(self.in_channels)

    # build laterals
    laterals = [#backbone输出特征图
        lateral_conv(inputs[i + self.start_level])
        for i, lateral_conv in enumerate(self.lateral_convs)
    ]
    laterals[0] = self.fpn_att[0](laterals[0])#将原始特征图经过注意力操作后，覆盖原始特征图，得到新的laterals[0]
    laterals[1] = self.fpn_att[1](laterals[1])
    laterals[2] = self.fpn_att[2](laterals[2])
    laterals[3] = self.fpn_att[3](laterals[3])
        #这里才是对特征图进行操作！！！！！！！！！！！！！！！！！！！！
    # build top-down path
    used_backbone_levels = len(laterals)
    for i in range(used_backbone_levels - 1, 0, -1):
        # In some cases, fixing `scale factor` (e.g. 2) is preferred, but
        #  it cannot co-exist with `size` in `F.interpolate`.
        if 'scale_factor' in self.upsample_cfg:
            # fix runtime error of "+=" inplace operation in PyTorch 1.10
            laterals[i - 1] = laterals[i - 1] + F.interpolate(
                laterals[i], **self.upsample_cfg)
        else:
            prev_shape = laterals[i - 1].shape[2:]
            laterals[i - 1] = laterals[i - 1] + F.interpolate(
                laterals[i], size=prev_shape, **self.upsample_cfg)

    # build outputs
    # part 1: from original levels
    outs = [
        self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
    ]
    # part 2: add extra levels
    if self.num_outs > len(outs):
        # use max pool to get more levels on top of outputs
        # (e.g., Faster R-CNN, Mask R-CNN)
        if not self.add_extra_convs:
            for i in range(self.num_outs - used_backbone_levels):
                outs.append(F.max_pool2d(outs[-1], 1, stride=2))
        # add conv layers on top of original feature maps (RetinaNet)
        else:
            if self.add_extra_convs == 'on_input':
                extra_source = inputs[self.backbone_end_level - 1]
            elif self.add_extra_convs == 'on_lateral':
                extra_source = laterals[-1]
            elif self.add_extra_convs == 'on_output':
                extra_source = outs[-1]
            else:
                raise NotImplementedError
            outs.append(self.fpn_convs[used_backbone_levels](extra_source))
            for i in range(used_backbone_levels + 1, self.num_outs):
                if self.relu_before_extra_convs:
                    outs.append(self.fpn_convs[i](F.relu(outs[-1])))
                else:
                    outs.append(self.fpn_convs[i](outs[-1]))
    return tuple(outs)

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